Air conditioning load prediction based on hybrid data decomposition and non-parametric fusion model

Abstract

Accurate prediction of air conditioning load is the pivotal problem of air conditioning optimization control, which is of great significance for reducing building energy consumption. In order to improve the prediction accuracy of air conditioning load, this paper proposes a load prediction method of air conditioning based on hybrid data decomposition (HRD) and non-parametric fusion model (NPFM). First, the historical load data is decomposed into linear term and nonlinear term by using HRD, which are employed to combine the temporal convolutional network (TCN) and long short term memory (LSTM) method to achieve the priori prediction of air conditioning load, and are regarded as the state transition equation. Secondly, the strong correlation index based on correlation screening is used as the observation parameter, and the nonlinear relationship between air conditioning load and real-time operating parameters of air conditioning equipment is mapped by back propagation (BP) neural network, which works as the observation equation, and forming a set of NPFM. Thirdly, the particle filter (PF) algorithm is introduced to correct the prior load prediction, suppress the noise disturbance, and realize the closed-loop control. Finally, the effectiveness of the proposed method is validated by two examples, and the results indicate that the proposed method can provide more accurate and robust air conditioning load prediction compared with existing methods.